
"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 			lowercase					( lowercase__		):
			lowercase        =     (DEISMultistepScheduler,)
			lowercase        =     (('''num_inference_steps''', 25),)






			def 					UpperCAmelCase							(self  :	str      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  {
				    '''num_train_timesteps''': 1_000,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''solver_order''': 2,
				}

				config.update(**SCREAMING_SNAKE_CASE_			)
				return config






			def 					UpperCAmelCase							(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	Any=0      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  dict(self.forward_default_kwargs			)
				lowerCAmelCase												=  kwargs.pop('''num_inference_steps'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.dummy_sample
				lowerCAmelCase												=  0.1 * sample
				lowerCAmelCase												=  [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					lowerCAmelCase												=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)
					scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)
					# copy over dummy past residuals
					lowerCAmelCase												=  dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(SCREAMING_SNAKE_CASE_			)
						lowerCAmelCase												=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_			)
						new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)
						# copy over dummy past residuals
						lowerCAmelCase												=  dummy_past_residuals[: new_scheduler.config.solver_order]

					lowerCAmelCase							,       lowerCAmelCase												=  sample, sample
					for t in range(SCREAMING_SNAKE_CASE_      ,time_step + scheduler.config.solver_order + 1			):
						lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample
						lowerCAmelCase												=  new_scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample

						assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Optional[int]:





				"""simple docstring"""


				pass






			def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  dict(self.forward_default_kwargs			)
				lowerCAmelCase												=  kwargs.pop('''num_inference_steps'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.dummy_sample
				lowerCAmelCase												=  0.1 * sample
				lowerCAmelCase												=  [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					lowerCAmelCase												=  self.get_scheduler_config()
					lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)
					scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

					# copy over dummy past residuals (must be after setting timesteps)
					lowerCAmelCase												=  dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(SCREAMING_SNAKE_CASE_			)
						lowerCAmelCase												=  scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE_			)
						# copy over dummy past residuals
						new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

						# copy over dummy past residual (must be after setting timesteps)
						lowerCAmelCase												=  dummy_past_residuals[: new_scheduler.config.solver_order]

					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample
					lowerCAmelCase												=  new_scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample

					assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"






			def 					UpperCAmelCase							(self  :	Dict      ,SCREAMING_SNAKE_CASE_  :	int=None      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	Optional[Any]:





				"""simple docstring"""


				if scheduler is None:
					lowerCAmelCase												=  self.scheduler_classes[0]
					lowerCAmelCase												=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  self.scheduler_classes[0]
				lowerCAmelCase												=  self.get_scheduler_config(**SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  10
				lowerCAmelCase												=  self.dummy_model()
				lowerCAmelCase												=  self.dummy_sample_deter
				scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

				for i, t in enumerate(scheduler.timesteps			):
					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			).prev_sample

				return sample






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	List[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  dict(self.forward_default_kwargs			)

				lowerCAmelCase												=  kwargs.pop('''num_inference_steps'''      ,SCREAMING_SNAKE_CASE_			)

				for scheduler_class in self.scheduler_classes:
					lowerCAmelCase												=  self.get_scheduler_config()
					lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  self.dummy_sample
					lowerCAmelCase												=  0.1 * sample

					if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE_      ,'''set_timesteps'''			):
						scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)
					elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE_      ,'''set_timesteps'''			):
						lowerCAmelCase												=  num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					lowerCAmelCase												=  [residual + 0.2, residual + 0.15, residual + 0.10]
					lowerCAmelCase												=  dummy_past_residuals[: scheduler.config.solver_order]

					lowerCAmelCase												=  scheduler.timesteps[5]
					lowerCAmelCase												=  scheduler.timesteps[6]

					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample
					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			).prev_sample

					self.assertEqual(output_a.shape      ,sample.shape			)
					self.assertEqual(output_a.shape      ,output_a.shape			)






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  DEISMultistepScheduler(**self.get_scheduler_config()			)
				lowerCAmelCase												=  self.full_loop(scheduler=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				assert abs(result_mean.item() - 0.2_39_16			) < 1e-3

				lowerCAmelCase												=  DPMSolverSinglestepScheduler.from_config(scheduler.config			)
				lowerCAmelCase												=  DPMSolverMultistepScheduler.from_config(scheduler.config			)
				lowerCAmelCase												=  UniPCMultistepScheduler.from_config(scheduler.config			)
				lowerCAmelCase												=  DEISMultistepScheduler.from_config(scheduler.config			)

				lowerCAmelCase												=  self.full_loop(scheduler=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				assert abs(result_mean.item() - 0.2_39_16			) < 1e-3






			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				for timesteps in [25, 50, 100, 999, 1_000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str			)       ->	Any:





				"""simple docstring"""


				self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_			)
				for order in [1, 2, 3]:
					for solver_type in ["logrho"]:
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    thresholding=SCREAMING_SNAKE_CASE_      ,prediction_type=SCREAMING_SNAKE_CASE_      ,sample_max_value=SCREAMING_SNAKE_CASE_      ,algorithm_type='''deis'''      ,solver_order=SCREAMING_SNAKE_CASE_      ,solver_type=SCREAMING_SNAKE_CASE_      ,)






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Any			)       ->	str:





				"""simple docstring"""


				for algorithm_type in ["deis"]:
					for solver_type in ["logrho"]:
						for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    solver_order=SCREAMING_SNAKE_CASE_      ,solver_type=SCREAMING_SNAKE_CASE_      ,prediction_type=SCREAMING_SNAKE_CASE_      ,algorithm_type=SCREAMING_SNAKE_CASE_      ,)
								lowerCAmelCase												=  self.full_loop(
								    solver_order=SCREAMING_SNAKE_CASE_      ,solver_type=SCREAMING_SNAKE_CASE_      ,prediction_type=SCREAMING_SNAKE_CASE_      ,algorithm_type=SCREAMING_SNAKE_CASE_      ,)
								assert not torch.isnan(SCREAMING_SNAKE_CASE_			).any(), "Samples have nan numbers"






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Dict:





				"""simple docstring"""


				self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_			)
				self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	List[Any]:





				"""simple docstring"""


				for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
					self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE_      ,time_step=0			)






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Tuple:





				"""simple docstring"""


				lowerCAmelCase												=  self.full_loop()
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				assert abs(result_mean.item() - 0.2_39_16			) < 1e-3






			def 					UpperCAmelCase							(self  :	Any			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  self.full_loop(prediction_type='''v_prediction'''			)
				lowerCAmelCase												=  torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

				assert abs(result_mean.item() - 0.0_91			) < 1e-3






			def 					UpperCAmelCase							(self  :	Optional[int]			)       ->	str:





				"""simple docstring"""


				lowerCAmelCase												=  self.scheduler_classes[0]
				lowerCAmelCase												=  self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE_      ,dynamic_thresholding_ratio=0			)
				lowerCAmelCase												=  scheduler_class(**SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  10
				lowerCAmelCase												=  self.dummy_model()
				lowerCAmelCase												=  self.dummy_sample_deter.half()
				scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

				for i, t in enumerate(scheduler.timesteps			):
					lowerCAmelCase												=  model(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  scheduler.step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			).prev_sample

				assert sample.dtype == torch.floataa




"""simple docstring"""






from ..utils import DummyObject, requires_backends



class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Union[str, Any]      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	str:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[int]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Union[str, Any]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	int      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	Any			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	int      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	int			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[str]      ,*SCREAMING_SNAKE_CASE_  :	List[str]      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	Dict:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Optional[int]      ,**SCREAMING_SNAKE_CASE_  :	Dict			)       ->	List[str]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Dict      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	List[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	Tuple      ,*SCREAMING_SNAKE_CASE_  :	Tuple      ,**SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)





class 			lowercase					( metaclass=lowercase__		):
			lowercase        =     ['''flax''', '''transformers''']






			def __init__(self  :	Optional[Any]      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	str			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(self      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	Union[str, Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)






			@classmethod
			def 					UpperCAmelCase							(cls  :	List[Any]      ,*SCREAMING_SNAKE_CASE_  :	List[Any]      ,**SCREAMING_SNAKE_CASE_  :	List[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				requires_backends(cls      ,['''flax''', '''transformers''']			)


